scholarly journals Economic development and material use. Evidence from international panel data

2019 ◽  
Vol 115 ◽  
pp. 107-119 ◽  
Author(s):  
Frank Pothen ◽  
Heinz Welsch
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
International Panel

PENGARUH PEMBANGUNAN INFRASTRUKTUR TERHADAP PDRB DI PROVINSI INDONESIA

Wahana ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 15-27
Author(s):  
Suripto Suripto ◽  
Eva Dwi Lestari
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Panel Data ◽  
Infrastructure Investment ◽  
Water Infrastructure ◽  
Infrastructure Development ◽  
Road Infrastructure ◽  
Health Infrastructure ◽  
Spatial Coverage ◽  
The Government

Economic growth is one indicator to measure  the success of economic development in a country. Economic development is closely related to infrastructure. Infrastructure development will have an impact on economic growth both directly and indirectly. Therefore, the role of the government in determining infrastructure development policies is very important to increase economic growth in Indonesia. The purpose of this study is to determine the effect of infrastructure on economic growth in Indonesia including road infrastructure, electricity infrastructure, investment, water infrastructure, education infrastructure and health infrastructure in Indonesia in 2015-2017.The analytical tool used in this study is panel data regression with the approach of Fixed Effect Model. The spatial coverage of this study is all provinces in Indonesia, namely 34 provinces, with a series of data from 2015 to 2017 with a total of 102 observations. The data used is secondary data obtained from BPS Indonesia.The results of the study show that (1) the road infrastructure variables have a negative and not significant effect on GDRP. (2) electrical infrastructure variables have a negative and not significant effect on GDRP. (3) investment variables have a positive and significant effect on GDRP. (4) water infrastructure variables have a positive and not significant effect on GDRP. (5) educational infrastructure variables have a positive and not significant effect on GDRP. (6) health infrastructure variables have a positive and significant effect on GDRP. Keywords: development, infrastructure, investment, GDRP, panel data

scholarly journals Tax Arrangement and Regional Industrial Restructuring: Evidence from Panel Data in China

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Jinying Ma ◽  
Xi Zhang ◽  
Feng Tao ◽  
Fuzheng Luo
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Income Tax ◽  
Industrial Structure ◽  
Econometric Model ◽  
Negative Impact ◽  
Industrial Restructuring ◽  
Development Mode ◽  
Secondary Industry ◽  
Regional Industrial Structure

Regional industrial restructuring has been one of the major items in the transformation of economic development mode. An exploration was made into the influence of tax arrangement on the regional industrial structure by setting up a panel data econometric model based on the evaluation and analysis of the regional industrial structure in China. It was shown that tax arrangement influenced the regional industrial restructuring in terms of three aspects. Microlevel: the turnover tax and income tax appeared with a U-path of influence on upgrading of the industrial structure while appearing with an inverted U-path of influence on rationalization of the industrial structure. In addition, the levy of resource tax had a negative impact on both upgrading and rationalization of the industrial structure. Mesolevel: taxation in the secondary and tertiary industries appeared with a U-path of influence on upgrading of the industrial structure. An increase of taxation in the secondary industry had a negative impact on rationalization of the industrial structure. The taxation in the tertiary industry appeared with an inverted U-path of influence on rationalization of the industrial structure. Macrolevel: the macrotax burden had a U-path of influence on both upgrading and rationalization of the industrial structure.

scholarly journals The Relationship between Environmental Regulation and Green Total Factor Productivity in China: An Empirical Study Based on the Panel Data of 177 Cities

2020 ◽  
Vol 17 (15) ◽  
pp. 5287 ◽  
Author(s):  
Mingliang Zhao ◽  
Fangyi Liu ◽  
Wei Sun ◽  
Xin Tao
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Inflection Point ◽  
Factor Productivity ◽  
Positive Effects ◽  
Development Level ◽  
The Mean ◽  
The Relationship

Promoting the coordinated development of industrialization and the environment is a goal pursued by all of the countries of the world. Strengthening environmental regulation (ER) and improving green total factor productivity (GTFP) are important means to achieving this goal. However, the relationship between ER and GTFP has been debated in the academic circles, which reflects the complexity of this issue. This paper empirically tested the relationship between ER and GTFP in China by using panel data and a systematic Gaussian Mixed Model (GMM) of 177 cities at the prefecture level. The research shows that the relationship between ER and GTFP is complex, which is reflected in the differences and nonlinearity between cities with different monitoring levels and different economic development levels. (1) The relationship between ER and GTFP is linear and non-linear in different urban groups. A positive linear relationship was found in the urban group with high economic development level, while a U-shaped nonlinear relationship was found in other urban groups. (2) There are differences in the inflection point value and the variable mean of ER in different urban groups, which have different promoting effects on GTFP. In key monitoring cities and low economic development level cities, the mean value of ER had not passed the inflection point, and ER was negatively correlated with GTFP. The mean values of ER variables in the whole sample, the non-key monitoring and the middle economic development level cities had all passed the inflection point, which gradually promoted the improvement of GTFP. (3) Among the control variables of the different city groups, science and technology input and the financial development level mainly had positive effects on GTFP, while foreign direct investment (FDI) and fixed asset investment variables mainly had negative effects.

scholarly journals Do Carbon Emissions and Economic Growth Decouple in China? An Empirical Analysis Based on Provincial Panel Data

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2411 ◽  
Author(s):  
Yu Hao ◽  
Zirui Huang ◽  
Haitao Wu
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Panel Data ◽  
Carbon Emissions ◽  
Environmental Kuznets Curve ◽  
Kuznets Curve ◽  
Ekc Hypothesis ◽  
Capita Gross Domestic Product ◽  
Level Of Economic Development ◽  
The Relationship

Global warming has emerged as a serious threat to humans and sustainable development. China is under increasing pressure to curb its carbon emissions as the world’s largest emitter of carbon dioxide. By combining the Tapio decoupling model and the environmental Kuznets curve (EKC) framework, this paper explores the relationship between China’s carbon emissions and economic growth. Based on panel data of 29 provinces from 2007 to 2016, this paper quantitatively estimates the nexus of carbon emissions and economic development for the whole nation and the decoupling status of individual provinces. There is empirical evidence for the conventional EKC hypothesis, showing that the relationship between carbon emissions and per capita gross domestic product (GDP) is an inverted U shape and that the inflection point will not be attained soon. Moreover, following the estimation results of the Tapio decoupling model, there were significant differences between individual provinces in decoupling status. As a result, differentiated and targeted environmental regulations and policies regarding energy consumption and carbon emissions should be reasonably formulated for different provinces and regions based on the corresponding level of economic development and decoupling status.

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